Biofluid analysis and classification using IR and 2D-IR spectroscopy
Biofluid analysis and classification using IR and 2D-IR spectroscopy
复制标题
DOI：
10.1016/j.chemolab.2021.104408
复制
发表时间：
2021-09-23
影响因子：
3.9
通讯作者：
Baker, Matthew J.
中科院分区：
文献类型：
--
作者：
Rutherford, Samantha H.;Nordon, Alison;Baker, Matthew J.
振动光谱由于其无标记和高通量的能力，为生物医学研究提供了有价值的信息。然而，近年来，光谱数据集的复杂性和大量变量使得多变量分析（MVA）和机器学习算法的应用越来越多。特别是，将这些技术应用于生物样品的红外光谱分析已被证明是快速样品分析和疾病诊断的有力工具。在本文中，我们回顾了用于分析生物流体红外 (IR) 光谱数据集的各种分类技术，并引用预测精度来证明其有效性。随着新技术的出现，二维红外光谱（2D-IR）最近已应用于生物医学问题，并显示出未来在生物流体分析中的潜在应用，然而，对于复杂的多维数据集，人们需要先进的分析技术。由于 2D-IR 在生物流体和生理蛋白质样品中的应用尚处于起步阶段，因此适合分类的生物流体的大型光谱数据集并不容易获得。必须确定 2D-IR 数据集以何种方式响应预处理和分析方法。我们首次利用本综述中讨论的应用于红外数据集的分类技术和相关的二维红外研究来讨论二维红外光谱中机器学习算法的未来。
Vibrational spectroscopy has produced valuable information for biomedical research owing to its label-free and high-throughput capabilities. However, the complexity of and large number of variables of spectral datasets has seen the increasing application of multivariate analysis (MVA) and machine learning algorithms in recent years. In particular, the use of these techniques applied to the analysis of IR spectra of biological samples has been demonstrated as a powerful tool for the rapid sample analysis and diagnosis of disease. In this article, we review a variety of classification techniques employed for the analysis of infrared (IR) spectral datasets of biofluids, quoting prediction accuracies to demonstrate their effectiveness. With the advent of new technologies, two-dimensional infrared spectroscopy (2D-IR) has recently been applied to biomedical problems and shows potential future applications in biofluid analysis, however with complex multi-dimensional datasets there is a desire for advanced analytical techniques. As the application of 2D-IR to biofluids and physiological protein samples is in its infancy, large spectral datasets of biofluids suitable for classification are not readily available. It is imperative to establish in what way 2D-IR datasets respond to pre-processing and analytical methods. For the first time we draw on the classification techniques applied to IR datasets discussed in this review and relevant 2D-IR studies to discuss the future of machine learning algorithms in 2D-IR spectroscopy.